A GNN-Based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks
TL;DR: In this paper , a graph neural network (GNN)-based framework is proposed to address the problem of resource allocation for D2D resource allocation in the Internet of Things (IoT).
read more
Abstract: The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN)-based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Posted Content
Graph-based Deep Learning for Communication Networks: A Survey.
TL;DR: A recent survey of graph-based deep learning methods for communication networks is presented in this article. But the focus of this survey is not on the application of deep learning in communication networks.
170
AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions
Sundas Iftikhar,Sukhpal Singh Gill,Chenghao Song,Minxian Xu,Mohammad Sadegh Aslanpour,Adel Nadjaran Toosi,Junhui Du,Huaming Wu,Shreya Ghosh,Deepraj Chowdhury,Muhammed Golec,Mohit Kumar,Ahmed M. Abdelmoniem,Felix Cuadrado,Blesson Varghese,Omer Rana,Schahram Dustdar,Steve Uhlig +17 more
TL;DR: In this article , the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments are analyzed using a systematic literature review (SLR).
Five Facets of 6G: Research Challenges and Opportunities
TL;DR: In this paper , the authors provide a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, and applications, as well as designs, and advocate a further evolutionary step toward multi-component Pareto optimization.
Innovative Trends in the 6G Era: A Comprehensive Survey of Architecture, Applications, Technologies, and Challenges
01 Jan 2023
TL;DR: In this article , the authors identify a complete picture of changes in architectures, technologies, and challenges that will shape the 6G network, and they hope the research results will provide indications for further studies on 6G ecosystems.
74
Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions
TL;DR: In this paper , the authors presented the state-of-the-art in quantum computing and provided comprehensive overviews through machine learning approaches for their applications in the 6G networks.
References
Graph Embedding-Based Wireless Link Scheduling With Few Training Samples
TL;DR: In this paper, a graph embedding-based method for link scheduling in D2D networks is proposed, which is based on the distances of both communication and interference links without requiring accurate channel state information.
129
Deep Reinforcement Learning Approaches for Content Caching in Cache-Enabled D2D Networks
TL;DR: The novel schemes based on deep reinforcement learning are proposed to implement the dynamic decision making and optimization of the content delivery problems, aiming at improving the quality of experience of overall caching system.
126
A Graph Neural Network Approach for Scalable Wireless Power Control
Yifei Shen,Yuanming Shi,Jun Zhang,Khaled Ben Letaief +3 more
- 01 Dec 2019
TL;DR: In this paper, an interference graph convolutional neural network (IGCNet) is proposed to learn the optimal power control in an unsupervised manner, which is a universal approximation to continuous set functions.
89
A graph-coloring secondary resource allocation for D2D communications in LTE networks
Dimitris Tsolkas,Eirini Liotou,Nikos Passas,Lazaros Merakos +3 more
- 22 Oct 2012
TL;DR: This paper studies how the traffic load between users located in the same cell (intra-cell communications) can be served by D2D transmissions utilizing uplink spatial spectrum opportunities, and shows that spatial Spectrum opportunities can sufficiently serve the intra-cell traffic, while increased data rates can be offered to the inter-cell Traffic.
88
Resource Management for Device-to-Device Communication: A Physical Layer Security Perspective
TL;DR: This paper analytically characterize the optimal power allocation of the CUs and D2D links, and develops efficient methods for joint optimization of their channel assignments, and shows that the proposed resource management policies outperform several baseline schemes and can indeed achieve the desired twofold objective.
87